Tetraamine-derived bifunctional chelators for technetium-99m labelling: synthesis, bioconjugation and evaluation as targeted SPECT imaging probes for GRP-receptor-positive tumours

Owing to its optimal nuclear properties, ready availability, low cost and favourable dosimetry, (99m)Tc continues to be the ideal radioisotope for medical-imaging applications. Bifunctional chelators based on a tetraamine framework exhibit facile complexation with Tc(V)O(2) to form monocationic species with high in vivo stability and significant hydrophilicity, which leads to favourable pharmacokinetics. The synthesis of a series of 1,4,8,11-tetraazaundecane derivatives (01-06) containing different functional groups at the 6-position for the conjugation of biomolecules and subsequent labelling with (99m)Tc is described herein. The chelator 01 was used as a starting material for the facile synthesis of chelators functionalised with OH (02), N(3) (04) and O-succinyl ester (05) groups. A straightforward and easy synthesis of carboxyl-functionalised tetraamine-based chelator 06 was achieved by using inexpensive and commercially available starting materials. Conjugation of 06 to a potent bombesin-antagonist peptide and subsequent labelling with (99m)Tc afforded the radiotracer (99m)Tc-N4-BB-ANT, with radiolabelling yields of >97% at a specific activity of 37 GBq micromol(-1). An IC(50) value of (3.7+/-1.3) nM was obtained, which confirmed the high affinity of the conjugate to the gastrin-releasing-peptide receptor (GRPr). Immunofluorescence and calcium mobilisation assays confirmed the strong antagonist properties of the conjugate. In vivo pharmacokinetic studies of (99m)Tc-N4-BB-ANT showed high and specific uptake in PC3 xenografts and in other GRPr-positive organs. The tumour uptake was (22.5+/-2.6)% injected activity per gram (% IA g(-1)) at 1 h post injection (p.i.). and increased to (29.9+/-4.0)% IA g(-1) at 4 h p.i. The SPECT/computed tomography (CT) images showed high tumour uptake, clear background and negligible radioactivity in the abdomen. The promising preclinical results of (99m)Tc-N4-BB-ANT warrant its potential candidature for clinical translation.

How stable are 20th century calibration models? A high-resolution summer temperature reconstruction for the eastern Swiss Alps back to A.D. 1580 derived from proglacial varved sediments

We found a significant positive correlation between local summer air temperature (May-September) and the annual sediment mass accumulation rate (MAR) in Lake Silvaplana (46°N, 9°E, 1800 m a.s.l.) during the twentieth century (r = 0.69, p < 0.001 for decadal smoothed series). Sediment trap data (2001-2005) confirm this relation with exceptionally high particle yields during the hottest summer of the last 140 years in 2003. On this base we developed a decadal-scale summer temperature reconstruction back to AD 1580. Surprisingly, the comparison of our reconstruction with two other independent regional summer temperature reconstructions (based on tree-rings and documentary data) revealed a significant negative correlation for the pre-1900 data (ie, late ‘Little Ice Age’). This demonstrates that the correlation between MAR and summer temperature is not stable in time and the actualistic principle does not apply in this case. We suggest that different climatic regimes (modern/‘Little Ice Age’) lead to changing state conditions in the catchment and thus to considerably different sediment transport mechanisms. Therefore, we calibrated our MAR data with gridded early instrumental temperature series from AD 1760-1880 (r = -0.48, p < 0.01 for decadal smoothed series) to properly reconstruct the late LIA climatic conditions. We found exceptionally low temperatures between AD 1580 and 1610 (0.75°C below twentieth-century mean) and during the late Maunder Minimum from AD 1680 to 1710 (0.5°C below twentieth-century mean). In general, summer temperatures did not experience major negative departures from the twentieth-century mean during the late ‘Little Ice Age’. This compares well with the two existing independent regional reconstructions suggesting that the LIA in the Alps was mainly a phenomenon of the cold season.

Impact of loading displacements on SLR-derived parameters and on the consistency between GNSS and SLR results

Displacements of the Earth’s surface caused by tidal and non-tidal loading forces are relevant in high-precision space geodesy. Some of the corrections are recommended by the international scientific community to be applied at the observation level, e.g., ocean tidal loading (OTL) and atmospheric tidal loading (ATL). Non-tidal displacement corrections are in general recommended not to be applied in the products of the International Earth Rotation and Reference Systems Service, in particular atmospheric non-tidal loading (ANTL), oceanic and hydrological non-tidal corrections. We assess and compare the impact of OTL, ATL and ANTL on SLR-derived parameters by reprocessing 12 years of SLR data considering and ignoring individual corrections. We show that loading displacements have an influence not only on station long-term stability, but also on geocenter coordinates, Earth Rotation Parameters, and satellite orbits. Applying the loading corrections reduces the amplitudes of annual signals in the time series of geocenter and station coordinates. The general improvement of the SLR station 3D coordinate repeatability when applying OTL, ATL and ANTL corrections are 19.5 %, 0.2 % and 3.3 % respectively, w.r.t. the solutions without loading corrections. ANTL corrections play a crucial role in the combination of optical (SLR) and microwave (GNSS, VLBI, DORIS) space geodetic observation techniques, because of the so-called Blue-Sky effect: SLR measurements can be carried out only under cloudless sky conditions—typically during high air pressure conditions, when the Earth’s crust is deformed, whereas microwave observations are weather-independent. Thus, applying the loading corrections at the observation level improves SLR-derived products as well as the consistency with microwave-based results. We assess the Blue-Sky effect on SLR stations and the consistency improvement between GNSS and SLR solutions when ANTL corrections are included. The omission of ANTL corrections may lead to inconsistencies between SLR and GNSS solutions of up to 2.5 mm for inland stations. As a result, the estimated GNSS–SLR coordinate differences correspond better to the local ties at the co-located stations when applying ANTL corrections.

Spatial gradients of temperature, accumulation and delta18O-ice in Greenland over a series of Dansgaard-Oeschger events

Air and water stable isotope measurements from four Greenland deep ice cores (GRIP, GISP2, NGRIP and NEEM) are investigated over a series of Dansgaard–Oeschger events (DO 8, 9 and 10), which are representative of glacial millennial scale variability. Combined with firn modeling, air isotope data allow us to quantify abrupt temperature increases for each drill site (1σ = 0.6 °C for NEEM, GRIP and GISP2, 1.5 °C for NGRIP). Our data show that the magnitude of stadial–interstadial temperature increase is up to 2 °C larger in central and North Greenland than in northwest Greenland: i.e., for DO 8, a magnitude of +8.8 °C is inferred, which is significantly smaller than the +11.1 °C inferred at GISP2. The same spatial pattern is seen for accumulation increases. This pattern is coherent with climate simulations in response to reduced sea-ice extent in the Nordic seas. The temporal water isotope (δ18O)–temperature relationship varies between 0.3 and 0.6 (±0.08) ‰ °C−1 and is systematically larger at NEEM, possibly due to limited changes in precipitation seasonality compared to GISP2, GRIP or NGRIP. The gas age−ice age difference of warming events represented in water and air isotopes can only be modeled when assuming a 26% (NGRIP) to 40% (GRIP) lower accumulation than that derived from a Dansgaard–Johnsen ice flow model.

Contribution of Starlette, Stella, and AJISAI to the SLR-derived global reference frame

The contribution of Starlette, Stella, and AJI-SAI is currently neglected when defining the International Terrestrial Reference Frame, despite a long time series of precise SLR observations and a huge amount of available data. The inferior accuracy of the orbits of low orbiting geodetic satellites is the main reason for this neglect. The Analysis Centers of the International Laser Ranging Service (ILRS ACs) do, however, consider including low orbiting geodetic satellites for deriving the standard ILRS products based on LAGEOS and Etalon satellites, instead of the sparsely observed, and thus, virtually negligible Etalons. We process ten years of SLR observations to Starlette, Stella, AJISAI, and LAGEOS and we assess the impact of these Low Earth Orbiting (LEO) SLR satellites on the SLR-derived parameters. We study different orbit parameterizations, in particular different arc lengths and the impact of pseudo-stochastic pulses and dynamical orbit parameters on the quality of the solutions. We found that the repeatability of the East and North components of station coordinates, the quality of polar coordinates, and the scale estimates of the reference are improved when combining LAGEOS with low orbiting SLR satellites. In the multi-SLR solutions, the scale and the Z component of geocenter coordinates are less affected by deficiencies in solar radiation pressure modeling than in the LAGEOS-1/2 solutions, due to substantially reduced correlations between the Z geocenter coordinate and empirical orbit parameters. Eventually, we found that the standard values of Center-of-mass corrections (CoM) for geodetic LEO satellites are not valid for the currently operating SLR systems. The variations of station-dependent differential range biases reach 52 and 25 mm for AJISAI and Starlette/Stella, respectively, which is why estimating station dependent range biases or using station-dependent CoM, instead of one value for all SLR stations, is strongly recommended.This clearly indicates that the ILRS effort to produce CoM corrections for each satellite, which are site-specific and depend on the system characteristics at the time of tracking,is very important and needs to be implemented in the SLR data analysis.

Time-Series Panel Analysis (TSPA): Multivariate Modeling of Temporal Associations in Psychotherapy Process.

Objective: Processes occurring in the course of psychotherapy are characterized by the simple fact that they unfold in time and that the multiple factors engaged in change processes vary highly between individuals (idiographic phenomena). Previous research, however, has neglected the temporal perspective by its traditional focus on static phenomena, which were mainly assessed at the group level (nomothetic phenomena). To support a temporal approach, the authors introduce time-series panel analysis (TSPA), a statistical methodology explicitly focusing on the quantification of temporal, session-to-session aspects of change in psychotherapy. TSPA-models are initially built at the level of individuals and are subsequently aggregated at the group level, thus allowing the exploration of prototypical models. Method: TSPA is based on vector auto-regression (VAR), an extension of univariate auto-regression models to multivariate time-series data. The application of TSPA is demonstrated in a sample of 87 outpatient psychotherapy patients who were monitored by postsession questionnaires. Prototypical mechanisms of change were derived from the aggregation of individual multivariate models of psychotherapy process. In a 2nd step, the associations between mechanisms of change (TSPA) and pre- to postsymptom change were explored. Results: TSPA allowed a prototypical process pattern to be identified, where patient's alliance and self-efficacy were linked by a temporal feedback-loop. Furthermore, therapist's stability over time in both mastery and clarification interventions was positively associated with better outcomes. Conclusions: TSPA is a statistical tool that sheds new light on temporal mechanisms of change. Through this approach, clinicians may gain insight into prototypical patterns of change in psychotherapy.

Chow–Liu trees are sufficient predictive models for reproducing key features of functional networks of periictal EEG time-series

Seizure freedom in patients suffering from pharmacoresistant epilepsies is still not achieved in 20–30% of all cases. Hence, current therapies need to be improved, based on a more complete understanding of ictogenesis. In this respect, the analysis of functional networks derived from intracranial electroencephalographic (iEEG) data has recently become a standard tool. Functional networks however are purely descriptive models and thus are conceptually unable to predict fundamental features of iEEG time-series, e.g., in the context of therapeutical brain stimulation. In this paper we present some first steps towards overcoming the limitations of functional network analysis, by showing that its results are implied by a simple predictive model of time-sliced iEEG time-series. More specifically, we learn distinct graphical models (so called Chow–Liu (CL) trees) as models for the spatial dependencies between iEEG signals. Bayesian inference is then applied to the CL trees, allowing for an analytic derivation/prediction of functional networks, based on thresholding of the absolute value Pearson correlation coefficient (CC) matrix. Using various measures, the thus obtained networks are then compared to those which were derived in the classical way from the empirical CC-matrix. In the high threshold limit we find (a) an excellent agreement between the two networks and (b) key features of periictal networks as they have previously been reported in the literature. Apart from functional networks, both matrices are also compared element-wise, showing that the CL approach leads to a sparse representation, by setting small correlations to values close to zero while preserving the larger ones. Overall, this paper shows the validity of CL-trees as simple, spatially predictive models for periictal iEEG data. Moreover, we suggest straightforward generalizations of the CL-approach for modeling also the temporal features of iEEG signals.